System and method for real estate spatial data analysis

ABSTRACT

A system and method for providing analysis on geocoded objects through collection, distribution and use of information through a user interface which allows the user to visualize the analysis through maps, symbols, text, and colors. In the context of commercial real estate, the system and method provides the user with a visual display and printout which allows the user to make decisions as to where retail locations should be located.

FIELD OF THE INVENTION

The present invention broadly relates to the field of geo spatial dataanalysis and more specifically to a system and method for analysis ofcommercial real estate using a variety of geocoded analytics.

BACKGROUND OF THE INVENTION

The market for real estate information analysis is in its infancy.Traditionally, the real estate model has been broker-centric. The localreal estate broker controls much of the localized information about aproperty becoming a necessary middle man for projects ranging frombuying a house to selling a shopping center. With the advent of theInternet and its efficient method for disseminating information, moreand more real estate information has become publicly available. Thisinformation, however, is spread out in multiple formats on variouswebsites, databases, and other sources. This makes it very difficult,time consuming and expensive to compile sufficient information to makereal estate purchase decisions. Real estate opportunities are oftenmissed because of the time it takes to get actionable information on asite.

Traditionally, real estate brokers and site selectors find potentialcommercial real estate sites for developers and tenants. Oneconventional method used by site selectors to find potential sites in anew area involves driving around the area and noting the location andquality of the different neighborhoods, and the location and quality ofthe existing commercial corridors. Thus the site selector will try toderive a potential store location's quality based on his observedquality of the surrounding area. The significant amount of time requiredto become familiar with an area is a reason why many developers andtenants turn to local real estate brokers for help. Another conventionalmethod used by site selectors is to mark locations of existing retailstores on a map. This conventional method helps the site selectordetermine how far away a potential site is located relative to anexisting retail store. One known approach used by commercial real estatesite selectors makes use of paper maps with stickers to indicate storelocations. Another known approach is to disseminate copies of books withhand marked store locations.

Once a developer or tenant's site selector finds a potential site, aconventional method involves ordering a demographic report from anin-house specialty team or outside consultant service. The demographicdata for the potential site is then compared to a tenant's stateddemographic requirements to determine demographic viability of a site.Additionally, tenants will often state how close they are willing toplace stores together. This stated distance is compared to the distancethe site selector marked for the site on his paper map to the knownstore locations. If the demographics and closest store distance meet thestated requirements for a particular retailer, then the developer willoften move forward with plans to acquire and present the site to atenant for development. These conventional methods must be repeated foreach potential site, creating significant time and cost inefficiencies.

The problem with this conventional method of determining site viabilityis that there is a significant information and time gap between the siteselector's first observation of a site and the developer's acquisitiondecision.

In view of the foregoing, there is a need to overcome the limitations ofthe conventional methods for finding site locations and determining siteviability. There is a need to efficiently inform a site selector of thequality and location of the neighborhoods and commercial corridorswithout the site selector having to drive throughout the area or dependon a local broker. There is a further need to inform the site selectorof a retailer's demographic and closest store distance requirements in alocalized region, not just a generalized stated requirement. There is afurther need to inform the site selector at the time of firstobservation whether the site's demographics and location to the nearestexisting retail store meet the requirements of a particular retailer ina particular region. There is a further need to create a standardunified model to collect and disseminate site information throughout acommercial development organization to facilitate efficient siteacquisition decisions.

SUMMARY OF THE INVENTION

In the context of commercial real estate, the present invention aims tomake necessary decision making information available, almost instantly,to the decision maker in a format that is uniform and easy tounderstand.

The present invention provides a system and method for analyzinggeospatial variables. In a commercial real estate context, the systemprovides methods for determining location criteria when analyzing realestate locations. These methods include: a method for evaluating apotential real estate site based upon an average distance betweenexisting retailer locations; A method for viewing existing retailertrade areas on a map based on an average distance between existingretailer location; a method for evaluating a potential real estate sitebased upon an average demographic variable value among existing retailerlocations; a method for viewing existing retailer trade areas on a mapbased on an average demographic variable value among retailer locationsin a region; a method to view property values on a map; and a method forcoloring a map based upon business types located in a geographic area.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 shows the overall system architecture of the present invention,according to an exemplary embodiment of the present invention.

FIG. 2 shows an exemplary data display illustrating an exemplaryembodiment of an interface which displays a map with current locationinformation and data to a user.

FIG. 3 shows an exemplary data display illustrating an exemplaryembodiment of an interface to record information about a real estatelocation.

FIG. 4 shows an exemplary data display illustrating an exemplaryembodiment of an interface displaying satellite imagery of a real estatesite and options to record information about a real estate site.

FIG. 5 shows an exemplary data display illustrating an exemplaryembodiment of an interface to generate information and data used in theoverall system architecture.

FIG. 6A shows an exemplary data display illustrating an exemplaryembodiment of an interface used to display data and a map for makingreal estate decisions and analysis.

FIG. 6B shows an exemplary data display illustrating an exemplaryembodiment of an interface used to display data and a map for makingreal estate decisions and analysis.

FIG. 7 shows an exemplary data display illustrating an exemplaryembodiment of an interface used to display demographic and geospatialvariable comparisons.

FIG. 8 shows an exemplary data display illustrating an exemplaryembodiment of an interface used to display demographic and geospatialvariable comparisons.

FIG. 9 shows a flowchart of a method illustrating an exemplaryembodiment to compare a retailer's distance requirements in a region tothe distance between a potential real estate site and the nearest storelocation of the retailer.

FIG. 10 shows a flowchart of a method illustrating an exemplaryembodiment for viewing existing retailer trade areas on a map based onan average distance between retailer locations.

FIG. 11 shows an exemplary data display illustrating an exemplaryembodiment of a map created by a method for viewing existing retailertrade areas.

FIG. 12 shows a flowchart of a method illustrating an exemplaryembodiment to compare a retailer's demographic requirements in a regionto a potential site's demographics.

FIG. 13 shows a flowchart of a method illustrating an exemplaryembodiment for viewing existing retailer trade areas on a map based on aretailer locations' average demographic variable values.

FIG. 14 shows a flowchart of a method illustrating an exemplaryembodiment for viewing property values on a map.

FIG. 15 shows an exemplary data display illustrating an exemplaryembodiment of a map created by a method for viewing property values on amap.

FIG. 16 shows an exemplary data display illustrating an exemplaryembodiment of a map created by a method for coloring a map based onactual land use.

FIG. 17A shows a flowchart of a method illustrating an exemplaryembodiment for coloring a map based on actual land use.

FIG. 17B shows a flowchart of a method illustrating an exemplaryembodiment for coloring a map based on actual land use.

FIG. 18 shows an exemplary data display illustrating an exemplaryembodiment of a display to print output created by the systemarchitecture.

DETAILED DESCRIPTION OF AN EXEMPLARY EMBODIMENT

The system of the present invention, includes an exemplary embodimentthat enables buyers and sellers to identify and analyze commercial realestate opportunities. The system includes, but is not limited to, thefollowing: a large scale database of business locations, associateddemographics and environmental variables; a user interface for selectingwhich locations, demographics, and environmental variables to analyze; auser interface and method for displaying the results of such analysis;and, a method for the collection and distribution of source data andanalysis. The system provides an efficient and detailed analysis bypresenting the user with a method to collect and store data in thefield, produce analysis to determine the viability of a commercial realestate site or project, and save the results for future display,distribution, and review. By providing a unified data model and a systemfor forming a variety of queries against the unified database, it ispossible to understand with precision the relationship between marketfactors that have heretofore only been understood in an anecdotal way.In this sense, the present invention resides in the interconnection ofrelated pieces of information that allows a true understanding and deepappreciation of a commercial real estate market. The user of the systemof the present invention has the ability to understand data in contextbecause the data in one data source is influenced by other data sourcesthat have heretofore not been connected.

FIG. 1 shows the overall system architecture 100 of an exemplaryembodiment of the present invention. As shown, the principal componentsof the system architecture 100 include: data mining applications 142,data sources and storage mechanism 110, database processes 148, a userinterface 162, and internet integration applications 164. The userinterface 162 directs data mining applications 142 to obtain commercialreal estate and other information from data sources 110. The data miningapplications 142 gather, organize, and transmit the information to acentral core data warehouse 144 or core user database on a localcomputer 146 where data processes 148 access the information andorganize it for manipulation by application processes 150 and the userinterface 162 or internet integration application 164 which thenpresents the information to the end user for review and manipulation. Interms of input and output, data sources 110, the user input interface127 and data mining applications 142 represent the input side of thesystem architecture while data processes 148, the internet integrationapplication 164, user interface 162, and printouts represent the outputside to which an end user of the system is connected.

Data Sources 110 include but are not limited to proprietary databases120, the Internet 122, on-site inspections 124, satellite images 126,aerial images 126, fly by inspections in an airplane or helicopter 124,public records 128, land use data 130, parcel information 132, federaldata providers such as the USGS 134, real estate listings from theMultiple Listing Service (“MLS”) and other providers 136, commercialdatabases and information sources 138, historical weather information,confidential user provided data such as proprietary sales information140, and bank deposit information. By conducting continuous, periodicpollings of data sources 110, the data mining application 142 ensuresthat the core data warehouse 144 and core user database 146 containup-to-date-information. In a networked environment, each user's computercontains a core user database 146. Each change to a core user database146 such as adding a site location, is uploaded to the core datawarehouse 144 where information may be distributed throughout theenterprise.

The data mining application 142 receives the information from datasources 110, including the Internet 122, into separate modules,applications, and tables including, in one exemplary embodiment of thepresent invention, a data collection and correction application 152; adrive application 154; a home prices application 155; an actual land useapplication 156; a demographics application 157; and, a retail analysisapplication 158 which includes average retailer demographics 159 and theaverage retailer distance 160 methods.

As data mining applications 142 receive real estate information andother information from data sources 110 and process the impact of thatinformation throughout the modules or applications, the information isstored and constantly updated in a central core data warehouse 144 andeach core user database 146. Database processes 148 access this datafrom the output side of core data warehouse 144 and core user database146 and create database sets compatible with formats required by each ofthe aforementioned applications 150. Each application manipulates thedatabase sets in response to commands from a user and presents theresults of database manipulations, e.g., search query results, to theuser in the form of a graphical user interfaces.

The specific manipulations executed by each of the applications aredescribed below in more detail.

The commercial real estate process will now be described beginning withprior art processes for commercial real estate selection andacquisition. This provides context in which an exemplary embodiment ofthe present invention operates. Current prior art inefficiencies in themarket include the methods traditionally used to select and acquirecommercial real estate. The first inefficient method describes how thecommercial real estate developer or his agent, (“site selectors”), willtypically drive around in a car for several hours a day and look forreal estate opportunities. If driving in an unfamiliar territory, siteselectors may get lost or have to stop and find the current location ona paper map. The paper map may not be detailed enough to show streetnames at the site selector's current location. One problem with thetraditional prior art method of scouting properties is often times youcannot see the whole property from the road. There may be hiddenopportunities or problems that are unseen from the roadway. Anotherinefficiency is the time period between the first observation of theproperty, and the act of recording the site information for furtherreview. Often, a site selector sees a property or real estate sign andjots down the information on a piece of paper. Many will try to call andget information while driving past the site. However, if no contact isavailable, the site selector will have to call the contact back in thefuture. The difficulty with this prior art method is that often thephone call to the contact is far enough in the future that the siteselector has forgotten her mental impressions of the property becausethere was no system in place to formally record the details of theproperty. The site selector must also determine to whom he will marketor sell the property, and provide a marketing study to show why theproperty would be a good fit for a particular tenant or purchaser. Thetraditional prior art method for accomplishing this task involvedordering a study which included demographics and tenant analysis. Thisstudy would take a significant amount of time and money to produce.Furthermore, the amount of information included in the reports wasfairly limited to complex and difficult-to-understand charts, along withlarge tables of demographics that listed what the site's demographicswere but did not include any analysis of tenant demographicrequirements. A conventional method of selling commercial real estateincludes significant marketing expense because the marketing involvespreparation of lengthy documentation and the system in place is nothighly automated. FIGS. 1-18 are slides that graphically depict anexemplary embodiment of the system 100 of the present invention. Thisexemplary embodiment of the system 100 and method operate within theabove-described commercial real estate market and transactional process.

The system 100 of the present invention will now be further described.The system 100 and method of the present invention allows users toperform analysis on geocoded objects through collection, distributionand use of information through a user interface 162 which allows theuser to visualize the analysis through maps, text, and colors. In anexemplary embodiment, a commercial real estate application, the system100 and method provides the user with a visual display and printoutwhich allows the user to make site location decisions and potentialtenant analyses. This method helps the real estate developer focus onthe best sites when presented with a number of site possibilities. Thisensures that sites selected will be quality sites to which the tenantswill want to locate and the developer may be able to sell for a profit.

In accordance with the system 100 and method of the present invention,the system 100 presents the user with an interface 127 to record sitesand choose which tenants and which tenant analyses to perform. In thiscontext, the user may compare site locations to the tenant analyses. Thesystem 100 will show the user which tenants could locate to the sitebased on the analyses performed.

One skilled in the art would naturally appreciate that this system ofanalysis is useful in contexts other than the commercial real estatecontext including, but not limited to, residential real estate location,and sales analysis.

Drive Application 154

Referring to the graphical depiction in FIG. 2-4, the Drive Application154 and its various components will now be described. An exemplaryembodiment of the Drive Application 154, provides a method for the siteselector to record site locations and site information. The DriveApplication 154 can be installed on a computer located in a vehicle withthe site selector. The advantage of using the system in a car or vehicleis that the user can determine his location and perform analysis whilesitting in front of or driving by a site. This instantaneous analysis iscritical to determining which retailers could utilize the location, andthe developer, site selector, and broker can determine if further reviewis necessary. Further review could include making a phone call, takingextensive notes, filling out the interactive forms in the system, andsending a purchase contract to the landowner. The user's thoughts,impressions, and observations can be input into the Drive Application154 while the user is sitting in front of the site. This enables theuser to record more extensive and accurate facts about the site thanwould otherwise be possible if the user were to take notes by hand.

The Drive Application 154 has a unique layout of three interfaces thatare interconnected because they pass location data amongst the variousmodules. These modules include a “GPS” Active Map (Global PositioningSystem) 200 that tracks the current location of the vehicle 201 andprovides real time demographics 204, a Site Information Form 300 torecord data about a site, and a Satellite Map 400 that also tracks thecurrent location of the vehicle. Each part of the interface is easilyaccessible by pressing a button or a tab. When the button or tab isdepressed, the respective module will display. Any number of thesemodules can be displayed at one time. The user can quickly switchbetween views in order to facilitate complete documentation of a site,in the least amount of time. The specific aspects of the DriveApplication will now be described.

GPS Active Map 200

Referring now to FIG. 2, one aspect of an exemplary embodiment of thepresent invention is the capability of the Drive Application 154 torecord information about the user's present location and any nearbycommercial real estate property. The Drive Application 154 accomplishesthis by showing the user his real time position on a map 201 using a GPSdevice to determine location. The real time position 201 indicatorprevents the user from becoming lost or requiring him to find hislocation on a physical map. The GPS device can be embedded in thecomputer, connected by a wire, or provided via wireless connection. Thecomputer may also receive location information from the vehicle'sonboard location/navigation system and the user's cellular telephone.The map 200 will draw a trail 202 so the user can see where he has been.The GPS Device records latitude and longitude and plots a trail on a mapinterface which allows the user to recall where he has been and where heneeds to go to find retail locations. This aspect is good for brokers,site selectors and others who need to know what territory they havecovered and what territory remains to be covered. The GPS trail 202 canbe saved and added to the core user database 146 of locations traveledalong with relevant information about the trip including: the date andtime of the trip, information recorded about sites on the trip,information recorded about existing locations, and a collection ofimages taken at a point in time simultaneously recorded with locationinformation. Users can see their real time location 201 in relation toexisting retailer locations that have been geocoded and displayed on themap 203. The map 200 can also be programmed to show other importantinformation to the user such as population density, traffic counts, homeprices 1500, and the actual land use 1600. By accessing the core userdatabase 146, the GPS Active Map 200 can show the user real timedemographics 204 or other variables for the geospatial area around thecurrent location. This is accomplished by passing the real time latitudeand longitude information to a real time demographics application 157which queries the core user database 146 for demographics which fallwithin a specified distance from the real time latitude and longitudeposition. For example, this method could show, as the user is moving ina vehicle, a constantly changing number for population within a 3 mileradius of the current location, average household income within a 3 mileradius of the current location, how many businesses are located within a3 mile radius of the current location, the predominant type of businesswithin a 3 mile radius of the current location, the average price of ahome and how many homes are for sale within 3 miles of the currentlocation. As the vehicle moves these numbers will change as the latitudeand longitude of the vehicle's current position changes. The advantageto this aspect of the system is that the user can know, and the computercan alert the user automatically, if the user's current location meetsthe demographic criteria required by a specific retailer in thesurrounding area. The specific retailer's demographic criteria isdetermined by the average retailer demographics method 1200 described inmore detail under the heading “Average Retailer Demographics.” Using theGPS Active Map 200 the user can mark a position on the map where he seesa location of real estate about which he wants to record information.When the user clicks on the map, the location information of the clickedlocation is sent to the Site Information Form 300. This form 300contains a number of fields that the user can fill out to recordinformation relevant to the site. Alternatively, the computer canautomatically generate information about the site using preloadedvariables such as retailer locations, stream locations, traffic countdata, and street data. Additional detail regarding the Site InformationForm 300 is described below in the subheading “Site Information Form”.An alternative aspect to this exemplary embodiment is that the user canclick anywhere on the GPS Active Map 200 to receive analysis on thatlocation, or to record information, and the user does not have to bephysically located by the site. Therefore it is possible, from anylocation with a computer and the system to mark a location on the map,record information about that location, and receive analysis on thatlocation from the system and method of the present invention.

Site Information Form 300

Referring now to FIG. 3, The Site Information Form 300 enables the userto quickly enter all relevant information about a site in a simple touse form. This method ensures that data collection is accurate,complete, and efficiently saved in the database. One aspect of the SiteInformation Form 300 is a touch screen interface that enables the userto quickly input site information. The Site Information Form contains anumber of fields that are relevant to analyzing and researching apotential commercial real estate site. These fields are arranged tofacilitate quick entry. Since most of the fields are predefined, only acheckbox or radio button selection 302 is required rather than freeformwords entered by the user. This method also allows the database to moreeasily perform analysis on the information entered into the fields.These fields include the potential developable lot size 303. Anotherexemplary embodiment lists the lot size 303 by the acreage required bydifferent tenants including a small drug store, a large drug store, asmall grocery store, a large grocery store, a specialty retail center,and a supercenter 303. Another field on the Site Information Form 300allows the user to select the current use of the site 304—whether it isa redevelopment to potentially buy the center and change the tenants orbuilding footprint, whether the site is empty in raw land form, whetherthe site is low use, i.e., not the best and highest use, and, whetherthe site is fragmented, meaning it has many owners. Another field allowsthe user to select access issues 305 such as whether there is a trafficlight—since many retailers require this; whether the site is on a cornerwith no light; and whether there is a problem with access to the site.Another field lists any physical problems 306 with the site such as agrading problem, the presence of power lines, the presence of wetlandsor streams, site visibility from the road, and the presence of acemetery on the site. Another field 307 prompts the user to list thesize of the road in front of the site in order to determine capacity,which shows the number of lanes. Additionally, the user can record 301the other uses on the adjacent and opposite corners of the intersectionin order to record the competition's location and alternative sitelocation possibilities. The layout of this section is unique because itis organized like a standard intersection 301 with areas for the user toinput the use on each corner. For each corner, the user may choose toinput from the following options: whether there is a major class Aretail tenant and if so, the user selects the tenant from a list 308;whether there is a gas station, a fast food restaurant, a used car lot;a house facing a busy road which would indicate a competing site; and, asolid subdivision which would indicate no competing use could occupy theother corner 309. Normally, the Site Information Form 300 orients theintersection layout in the following manner: the northwest corner on theupper left part of the interface, the southwest corner below it, thenortheast corner on the upper right side of the interface, and thesoutheast corner below it. An alternative exemplary embodiment to thislayout rotates the orientation of the layout as the direction of thevehicle changes. For example, when the vehicle is facing north, thenorthwest corner will be on the left side of the interface and thenortheast corner will be on the right. But when the vehicle is facingsouth, the southeast corner of the intersection will be on the upperleft side of the interface and the southwest corner will be on the upperright side of the interface. The direction will change similarly for aneast or west heading. Thus, the heading of the vehicle matches theheading of the intersection layout section of the Site Information Form300. This method ensures that the user correctly matches the use of eachcorner to its correct field on the interface. This method is possible byretrieving the vehicle's heading from a GPS reading and the GPS trailinformation 202 from the GPS Active Map 200. The Site Information Form300 also allows the user to record special notes 310 about the site, thelevel of traffic 311 at the site which is inputted manually by the useror derived automatically from traffic counts, on which corner the siteis located 312, the call history on the site 310, the site's priority,the name of the site 314, the latitude and longitude of the site 315,and the drive 316 on which the site was located, which is a time andlocation stamp of where and when the site was located.

Drive Application Satellite Map 400

Referring now to FIG. 4, a satellite map 400 is important to therecording of information in the field because it allows the user to seesome details or layout aspects of the site that are not visible from theroad. The satellite images may reside on the computer or be accessiblevia the internet which is accessed via a wireless or cellular internetconnection in the vehicle. When the user opens the satellite map 400 themap 400 reads the location from the GPS device or GPS Active Map 200 andautomatically zooms the satellite view 401 to the location of the sitethat has been marked 402, or the current vehicle position 201. The userhas the ability to draw a polygon 403 around the site and save thepolygon 404 to the database for future use. The system calculates thesquare footage area 405 of the site based on the coordinates of thepolygon and saves this value to the database. This value can later beused to perform analysis and select sites that meet certain acreagerequirements.

Geospatial Variable Selector Interface 500

Referring now to FIG. 5, an exemplary embodiment of the presentinvention provides a user method to select geospatial variables toanalyze. The Geospatial Variable Selector Interface 500 is a first stepto identify a retailer's required demographic and location criteria fora site. This exemplary embodiment is described in a commercial realestate context but may be used in other exemplary embodiments as well.The user may employ the Geospatial Variable Selector Interface 500 toanalyze relationships between sites recorded using the drive application154 or sites selected off a map 200, and geospatial variables 504.Geospatial variables include demographic information and other sites andretail locations. The system and method can help the user identifylocations to which the user should drive to get more detailed notes on apotential site that meets the criteria of the targeted retailers.

The user is presented with a form on a visual display 500 and is able toselect a geocoded object(s) 502 on which to run an analysis. A geocodedobject in a commercial real estate context would be a site location oran existing retail, industrial, or office location or groups oflocations. The user may obtain objects from a list or search interface502. The user then chooses the analysis to run from the analysis section504 of Geospatial Variable Selector Interface 500. Analysis optionsinclude but are not limited to, distance relationships between selectedgeocoded objects, demographic data related to geocoded objects, andstatistical relationships. Examples of analysis options in a commercialreal estate context include: 1, 3, and 5 mile population; 1, 3, and 5mile household income; and a 1, 3, and 5 mile household postal dropcount analysis. The methods for calculating these analyses are describedbelow under the headings Average Retailer Distance 900 and AverageRetailer Demographics 1200. Groups of pre-defined geocoded objects andanalyses may be preset 501 so the user can quickly choose an analysispackage to run. For example, the user could create a preset called“Grocery Stores and 1, 3, 5 mile Population” 507 and then chooseretailers to analyze from the list or from a search 502. For thepurposes of this example let's assume that the user selects Publix andKroger 503, two grocery store chains in business at the time of thiswriting, from a predetermined list of retailers. After choosing theretailers, the user would select the analysis to be performed from theanalysis list 504. In this case, the user selects the 1 Mile Population,3 Mile Population, and 5 Mile Population analyses to be performed. Theuser saves the setup 505 and instructs the system to perform thecalculations that correspond to the locations and analyses chosen 506.If the user desires he can now create a separate preset called “GroceryStores and 10 Mile Population” having a variety of geocoded storeobjects and analyses saved as presets to be run. This saves time for theuser because presets can be quickly changed in the field, whereasgenerating a preset takes more time. Other uses of presets would beevident to one skilled in the art.

Commercial Real Estate Analysis Interface 600

Referring now to FIGS. 5, 6A, and 6B, once the user has selected apreset 501, the user opens the Commercial Real Estate Analysis Interface600. The content of this interface 601 is automatically generated basedon the user's preset choice 501. For example, if the preset 501 containstwo stores 503 for location analysis, the tab section of the real estateanalysis interface 601 will include two tabs, one for each store. In afurther aspect of this exemplary embodiment, the user may select from alist those stores and variables which should be displayed in the realestate analysis interface 600. These selections will populate on theinterface tabs 601.

There are several advantages of using a tabbed interface 601 to analyzemaps and information. A tabbed analysis interface 601 provides theability to quickly scan through tabs at the same height. The tabs caninclude maps 604, each zoomed to the same location and height for a realestate object being analyzed. This allows the user to quickly scanbetween tabs 601 and understand the information presented on top of themap 604. The result, the user does not have to reprocess the backgroundmap 604 in his mind (which doesn't change); rather, he can focus on thedata being presented.

A benefit of the tabbed interface is explained in the following example.The user has several retailer trade area maps loaded with storelocations and a colored radius shape around each store. One tab 602 willinclude a map 604 of retailer #1 and have a radius drawn around each ofretailer #1's stores 605. The next tab 651 will include a map 650 ofretailer #2 and have a radius drawn around each of retailer #2's stores653. Each map is centered on the same point 652, in this instance, thepotential site location 652 for one of the 2 retailers. The user canscan through the tabs 601 by clicking on each, and by focusing his eyeson the site location 652 can see whether one of the colored radii 653from each retailer overlaps the location of the site 652. If the usersees a map 650 with no overlapping retailer radius he can conclude thatthe location may be a good location for that particular retailer 651.Areas of the map with no colored shapes are referred to as holes 654. Auser will often plan a site selection drive by opening a map 650 ofretailer circles 653, finding the holes 654 and plotting a route thatallows her to scout for properties in each of these holes. This methodsignificantly reduces the time spent scouting for sites 652, because thesite selector knows exactly where to go.

A further exemplary embodiment of The Commercial Real Estate AnalysisInterface provides the user with analysis search options 603 including:searching by site, searching by size, searching by drive, and searchingby map selection 606. The user may also choose an analysis 606 from alocation picked on an interactive map. The user can select thebackground interactive map 604. For example, the user could choose aparticular trade area map, actual land use map, or traffic map, or anycombination of the foregoing, as a background map 604. The user may thenmap data on top of this background information. The user may interactwith these maps, save the maps, and retrieve the maps for furtheranalysis

In one exemplary embodiment of The Commercial Real Estate AnalysisInterface 600, the user can create background maps 604 with circles orshapes around locations. In a commercial real estate context, circlesand shapes can be drawn around retail locations using the AverageDistance Trade Area method 1000 or the Average Demographic Trade Areamethod 1300. The user benefits from these methods when looking at a mapsince a user can look for holes 654 which are not shaded by a retailer'sradius 653 and know that a retail location may be able to go there. Forexample, the user chooses the site on the map 650, and runs an analysis.The Application Processes 150 draw circles around the retail locationsand shades the circles a solid color. The user can also manuallydetermine the size of the circles 653 around each retailer. Forinstance, the user may want to see a map 650 where the circles 653around grocery stores have a radius of 2 miles, and another map whichshows the radius around drug stores as 1 mile. The radius can also bedetermined by using Average Distance Trade Area method 1000. In thisexemplary embodiment, the circles shown to the user on a map will have aradius equal to half the average distance between the stores. Eachretailer will therefore have a custom radius size 653 based on theaverage distance 1009 between it's stores in a given area specified bythe user for analysis 1006. The radius may also be determined by theaverage demographic variable function described under AverageDemographic Trade Area method 1300. In this exemplary embodiment, thecircles 653 will encompass an area which meets the average demographicvariable. For example, in a given area specified by the user, a retailermay have an average 3 mile population of 20,000. The program will drawcircles around each of the retailer's stores until size of the circleencompasses an area which has a population of 20,000. In some cases, itmay not take a very large circle to encompass 20,000 people, andtherefore two stores may be able to go in a high population density areaand have circles that do not overlap each other.

Referring now to FIG. 7, a further exemplary embodiment 700 of TheCommercial Real Estate Analysis Interface 600 displays the geospatialvariable analyses selected by the user 701. These include but are notlimited to: the site demographics 702, the average retailer demographics703, and the average retailer distance 704. The methods that generatethe displayed information 700 are described below. One exemplaryembodiment 700 compares the site's demographics 702 to the averageretailer demographics 703, and the site's distance to the nearestretailer 705 to the average retailer distance 706. The result isdisplayed in a format that the user can easily discern which retailerswill work best for the site, and which retailers are ill-suited for thesite. One exemplary embodiment 700 may use color shading to representwhether the site's demographics 702 and specified variables 701 meet theaverage store demographics 703 and variables 701 in the area. If thevalues for site's demographic variable values 702 meet or exceed theaverage demographic variable values 703 for a particular retail store inthe area, then the color of the row in the table will be shaded green.If the site's demographic variable value 702 falls below the retailervalue 703, then a color of yellow or red will be assigned to the row tovisually warn the user that the site value 702 does not meet the averagestore requirements 703 in that area. Alternatively, color coding can usestatistical methods such as standard deviation to assign colors to thecells.

Referring now to FIG. 8, another exemplary embodiment 800 to displaygeospatial variables analysis uses color coding and sorting to makeconclusions about the data easy to understand 800. The analysis 800 isdisplayed to the user in a table format. In a commercial real estatecontext, this method can help the site selector find a tenant. Thismethod ranks the tenants in order of how well the site meets theirdemographic requirements. The columns 801 represent the demographicvariables used to calculate the average retailer demographics 1200. Therows represent each retail store analyzed 802. Each cell in the tablecontains the value of the variable for the respective retail store 803.These values 803 are the demographic requirements of the retailer 804for the particular variable value 801. If the values 807 for site'sdemographic variable values 807 meet or exceed the average demographicvariable values for a particular retail store in the area, then thecolor of the cell for the retailer value in the table will be shadedgreen 808. If the site's demographic variable value falls below theretailer value, then a color of yellow or red will be assigned tovisually warn the user that the site does not meet the average storerequirements in that area. Alternatively, color coding can usestatistical methods such as standard deviation to assign colors to thecells. In addition to assigning colors to the cells, the table will alsobe sorted so that the retailer 804 whose demographic and distancerequirements the site meets, will be at the top of the list, and theretailer whose demographic and distance requirements the site does notmeet, will be at the bottom 805. Thus, the user will see a table withgreen cells at the top and yellow and red cells at the bottom. The tablealso sorts and shades cells based on the closest existing retail storelocation and the average distance between that retailer's stores 806.

Average Retailer Distance Method 900

This method 900 illustrated in the flowchart of FIG. 9, returns theaverage distance between geocoded objects in a given area. Described in,but not limited to a commercial real estate context, this method 900returns the average distance 908 between stores of a particular retailerin a given area 904. This distance value 908 is useful to determine thenecessary minimum distance between a potential commercial real estatesite and the closest existing retail store location 911. Most retailersdo not want to put their store locations too close together becauseoverlapping trade areas reduce each store location's potential profit.Many retailers have their own internal criteria for determining howclose the stores should be placed together. Using this method 900, theuser can understand each retailer's minimum distance requirements for agiven region 904.

The method 900 illustrated in the flowchart in FIG. 9 describes themethod 900 to compare a retailer's requirement of minimum distancebetween store locations 908 in a region 904 to the distance between apotential site location and the nearest store location 911. A userselects a potential site location 901, and a prospective retailer 903 tocompare. Minimum distance requirements for a retailer may vary fromregion to region so the region size 904 to compare distances may beselected manually by the user 905, or automatically by the application906. A spatial query returns the prospective retailer's existing storelocations in the selected region 907. Another spatial query calculatesthe distance between each of the retailer's store locations and the nextclosest store location 909. These distance values are totaled andaveraged 910 resulting in a value that is the average distance between aretailer's locations in the region 908. The next step in the method 900is to determine the distance from the potential commercial real estatesite location to the nearest location of the retailer 911. A spatialquery makes this calculation 912. The next step 913 is to visuallycompare the nearest distance 911 to the average distance 908. Thenearest location distance 911 may be displayed adjacent to the averagedistance 914 in a table format 915. The rows may be colored a color suchas green or red depending on whether the nearest distance is less thanor greater than the average distance 916. Colors are useful to allow thecommercial real estate site selector or user determine whether thedistance values exceed or fall short without having to look at andmentally process the actual number.

Average Distance Trade Area Method 1000

Referring now to FIGS. 10 and 11, the method 1000 creates a map 1100 ofan existing retailer locations' trade areas 1101 based on the averagedistance 1009 between the retailer's locations 1003. The user selects anexisting retailer to analyze 1001 trade areas for each location, and theapplication 158 cycles through the method 1000 for each of theretailer's locations. The trade areas 1101 are based on the averagedistance 1009 between the retailer's locations in the region 1003.Minimum distance requirements for a retailer may vary from region toregion so the region size 1005 to compare distances may be selectedmanually by the user 1006, or automatically by the application 1007. Aspatial query returns the retailer's existing store locations in theselected region 1008. The geographic region is centered on the selectedexisting retail location 1004. Another spatial query calculates thedistance between each of the retailer's store locations and the nextclosest store location 1010. These distance values are totaled andaveraged 1011 resulting in a value that is the average distance betweena retailer's locations in the region 1009. The next step in the method1000 is to draw a circle around the selected retail location 1012. Thediameter of the circle is sized equal to the average distance value1013, and may also be sized by using the value 1014 obtained byaveraging the average distance 1015, and the distance between theselected location and an existing retailer location located in closestproximity to the selected location 1016.

This method 1000 creates a map 1100 with circles 1101 centered on eachof the retail locations 1101. In a dense urban area 1103, where storelocations are close together, the circles around each location will besmall because the average distance value 1009 is small, however in ruralareas 1104, where store locations are spaced very far apart, the circleswill be large because the average distance value 1009 is large. Circles1101 may be viewed as trade areas. Areas with no circles 1105 may betreated as holes where the retailer needs to add a store location. Thisenables the commercial site selector to target these holes to look forsites for a retailer with no trade area coverage there. Shapes otherthan circles can also be used.

Average Retailer Demographics 1200

Described in, but not limited to a commercial real estate context, themethod 1200 of FIG. 12 returns the average demographics of a group ofstores of a particular retailer in a given area. This average value 1208for each demographic variable is useful to determine the necessarydemographic requirements of a retailer in a given area.

Sources of demographic variables 110 are used for this method 1200.Demographic variable information may be associated with and stored by anumber of geographic areas including: census tract, census block, postalcarrier routes, states, metropolitan service areas, and zip codes,states, counties, and regions. Many demographic variables may bereturned as part of a demographic analysis. An example of three populardemographic variables in a commercial real estate context arepopulation, number of households, and household income.

Most retailers do not want to put their stores too close togetherbecause overlapping trade areas reduce each store location's potentialprofit. Demographics can help define trade areas. Ideally, each retailstore's trade area would pull from a population that does not overlapanother of the retail store's trade area population. Each retailer mayhave its own internal criteria for determining the demographicrequirements of its stores. Using this method, the user can understandeach retailer's minimum demographic requirements for a given area.

The method 1200 of the flowchart in FIG. 12 describes the method 1200 tocompare a retailer's demographic requirements in a region to a potentialsite's demographics. A user selects a potential site location 1201, anda prospective retailer 1203 to compare. Demographic requirements for aretailer may vary from region to region so the region size to comparedemographics may be selected manually by the user 1205, or automaticallyby the application 1206. A spatial query returns the prospectiveretailer's existing store locations in the selected region 1207.

In order to determine the average demographic variable values for theretailer's locations in the selected region 1208, the user will firstselect which demographic variables to analyze 1209. An exampledemographic variable would be Total Population within a 1 mile radius.The application calculates and returns the demographic variable valuesfor each of the existing retail store locations 1210. The values foreach demographic variable are totaled and averaged 1211. An exampleexpressed in a sentence: The average population within 3 miles of a CVSstore is 20,000 people.

The next step is to calculate the same demographic variables for thesite location 1212. Using a spatial query, the application returns thedemographic variable values for the potential site location 1213.

Once both the average demographic variable values for the potentialretailer 1208 and the site demographic variable 1212 values have beencalculated, the two sets can be compared visually 1214. One method toeffectively compare the two sets of values is to use a table format 1216to display the site demographics adjacent to the retailer's averagedemographics in the region 1215. The method may use colors to enhancethe visual effect; the rows may be colored green or red depending onwhether the site's values exceed or fall below the average store values1217.

An example of this method will now be described. First, the user selectsa site location 1201 and a retailer 1203 to compare. Next, the userselects a region 1204 to analyze: for example, a 10 mile radius from thesite. Next, the user selects a demographic variable 1209 to analyze:Population within 3 miles. Next, the method calculates the 3 milepopulation for each retail store location that lies within 10 miles fromthe site 1210. The method totals the 3 mile population values andcalculates an average 1211. This average is The Average RetailerDemographics Value 1208. The Average Demographics value 1208 is thencompared to the site's value 1212 for 3 mile population 1215. Thus, auser can determine whether the 3 mile population around the site 1212exceeds, meets, or fails to match up to the average 3 mile population1208 around the retailer's stores in the 10 mile region.

In a further exemplary embodiment of the Average Retailer Distance 900,and Average Retailer Demographics 1200 methods, both may be displayed tothe user in the following format 700: in one column 702, the selectedsite's variables are displayed 702, and in the adjacent column, theretail store's average variables (i.e. the store's criteria) aredisplayed 703. Formats other than columns are evident to one skilled inthe art. If the site's variables meet or exceed the average values inthe store's column, the cell of the site's value will be shaded green.If the site's variables fall within one standard deviation below that ofthe store's criteria, the cell will be shaded yellow, and if the sitesvariables fall greater than one standard deviation below the store'scriteria, then the cell will be shaded red. Other statistical methods oruser specified ranges may be used to shade the cells. This displaymethod allows the user to quickly see if the site's criteria meet orexceed that of the store's criteria. The user can look to see if thecolumn is all green without having to look at the actual numericalvalues. If one of the cells is yellow or red, the user will know thatthe area's demographics or store selection criteria may not meet thedemands of the particular retailer.

Average Demographic Trade Area Method 1300

Referring now to the flowchart of FIG. 13, the method 1300 creates a mapof an existing retailer locations' trade areas based on the averagedemographics 1309 among the retailer's locations 1303. The user selectsan existing retailer to analyze 1301 trade areas for each location, andthe application 158 cycles through the method 1300 for each of theretailer's locations. The trade areas are based on the averagedemographics 1309 among the retailer's locations in the region 1303.Minimum demographic requirements for a retailer may vary from region toregion so the region size 1305 to compare demographics among locationsmay be selected manually by the user 1306, or automatically by theapplication 1307. A spatial query returns the retailer's existing storelocations in the selected region 1308. The geographic region is centeredon the selected existing retail location 1304. In order to determine theaverage demographic variable values for the retailer's locations in theselected region 1309, the user will first select which demographicvariables to analyze 1310. An example demographic variable would beTotal Population within a 1 mile radius. The application calculates andreturns the demographic variable values for each of the existing retailstore locations 1311. The values for each demographic variable aretotaled and averaged 1312. An example expressed in a sentence: Theaverage population within 3 miles of a CVS store is 20,000 people. Thenext step in the method 1300 is to draw a circle around the selectedretail location 1313. The circle 1313, is sized by increasing thediameter until it encompasses a geographic area whose demographicvariable values meet or exceed the average variable values 1314.

Home Price Icons

Referring now to FIGS. 14 and 15, an additional exemplary embodiment ofthe present invention 100 displays Home Prices on a map. This method1400 provides the user with a visual display 1500 of Home Pricesrepresented by shaded icons with numbers on a map 1502. Unlikemanifestations of prior art, the map 1503 does not require a key orlegend 1413, because a price number is represented on the shaded icon1502. The icons use shading from one primary color 1411 to indicate theprice of the home relative to other home prices.

Referring now to the flowchart in FIG. 14 the method 1400 will now bedescribed. Data on home prices 1402 is collected from several sourcesincluding: publicly available sources on the internet 122, purchaseddata 138, property deed records 128 and sources such as the MultipleListing Service (“MLS”) 136. The data on the home prices is stored in atable 1401 which includes a geocoded location of the home and a pricevalue. Land Prices 1403 may be substituted for home prices 1402 in thismethod 1400. A query 1404 organizes the values 1401 into ranges. Withrespect to home price values 1402, an exemplary range may be defined in$100,000 increments. For example $100,000-$199,000. A numbered icon isassociated with each range 1406. Following the previous example, thenumber on the icon would be 1. Therefore the number on the icon is afraction of the range value 1407. The multiplier used to denote therange may be specified in the title of the map 1408. For example, thetitle of the map could read Home Prices in Hundred Thousands 1504. Anicon with a 2 on it 1502, would therefore indicate that the home pricewas between $200,000 and $299,000. The next step in the method 1400 isto associate a shade 1410 of a single primary color 1411 with eachrange. The benefit of using a shades from a single primary color is thatthe user does not have to reference a legend to understand therelationship between color and price, instead, the relationship betweenprice and color may be derived by simply looking at the map 1503. Thenext step in the method 1400 is to display an associated numbered icon1406 on a map at the location of each geocoded value 1412. Each icon isthen colored based on its range and associated shade of color.

The resulting map 1500 is displayed to the user. The shading of theicons allows the user to see patterns in home prices. For example, wherethe icon set uses a shade of the single primary color blue, aneighborhood where the home prices are in the $100,000 range will haveseveral pushpins that are light blue in color, whereas a neighborhoodwith home prices in the $1,000,000 range, with have several pushpinsthat are dark blue in color. If the user sees a dark blue area of themap 1503, he can infer that the homes are expensive without having tofocus on the actual price. If the user wants to know the price of thehome however, he merely needs to look at the icon's number whichrepresents the price of the home in the multiple specified in the title1504. For example, a title may say “Home Prices in Hundred Thousands($100,000) 1504. If an icon has the number 2 on it 1502, then the homeprice is in the $200,000-$299,999 range 1502. Other interpretations ofthe icon's number can be specified in the title. An important aspect ofthe icon's number is that the user can print the map in black and whiteand still understand the values, without the use of color.

This method 1400 also allows the user to find what are commonly referredto as gems in the rough—where a lightly shaded property is amongst acluster of dark properties, there is a high possibility that theproperty may be undervalued. The method 1400 is also used to discernneighborhood types based on the shading. When looking at a zoomed outview of a map 1503 with many Home Prices 1502 on it, the user will seepatterns of Home Prices that have similar shadings of color. Forexample, an area with dark shading will indicate that this neighborhoodis very expensive, whereas an area with lots of light shading willindicate that the neighborhood is very inexpensive.

An additional aspect of this exemplary embodiment is to display anaverage area home price in a textual display 1501 that constantlyupdates as the user drives around in a vehicle. An example in acommercial real estate context would be where the site selector drives acar looking for property to acquire. The interface 162 displays orthrough an audio interface, shows the site selector the average HomePrice for a 1, 3, and 5 mile radius from the site selector's currentlocation 1501. As the vehicle moves, the Home Price numbers will changeas the user passes different neighborhoods with different prices. Thisaspect of a moving update can also be used to display demographicinformation or other geospatial variable 404.

This method 1400 of viewing Home Price analysis can also enable the userto understand income and spending power in an area since the likelihoodof greater income and spending power is correlated with more expensivehomes, and the likelihood of lesser income and spending power iscorrelated with neighborhoods with less expensive home prices. Thismethod 1400 can help a retailer decide, visually, whether thesurrounding neighborhoods fit the income profile of their targetcustomer. This method could also be used for a variety of otherpurposes, as one skilled in the art would appreciate. For example, aconsumer application would allow consumers to find neighborhoods thatfit their price range. Helping consumers understand where all of thedifferent types of neighborhoods are would greatly minimize the need forthe traditional residential real estate agent.

Actual Land Use Map

Referring now to FIGS. 16, 17A, and 17B, a further exemplary embodimentof the present invention 100 creates an Actual Land Use Map 1600. TheActual Land Use Map or an As Built Map 1600 is a map which displays tothe user with colors and shapes, the predominant land use type in agiven incremental area of the map. The database 138 used to create theActual Land Use Map is populated with business data and telephonedirectory business data. A benefit of using the Actual Land Use Map overa zoning or future land use map is that the map displays how the land iscurrently being utilized. This map 1600 is useful to a commercial realestate site selector or decision maker who is unfamiliar with an areawhere the site selector is looking for sites. It helps identify thelocation, type and quality of commercial corridors. Combined with thehome price map, a site selector can identify strong commercial corridorsand their relation to the location and quality of neighborhoods.

The Actual Land Use Map 1600 can be generated by a computer or by hand,and the result can be displayed on a computer visual interface 162 or ina printout paper map. One exemplary embodiment of the Actual Land UseMap 1600 is to use this business data/color system in a commercial realestate context to create an actual land use map 1600 by defining thecategories as retail, industrial, distressed, and office 1602.

Referring now to FIG. 17A, a method 1700 for creating this map 1600 willnow be described in general, with additional detail following below andin FIG. 17B. A commercial land use type is assigned to each business ina database 1701. A selected area of the map 1702 is divided intoincremental shaped areas 1703. A spatial query 1704 returns thebusinesses located within each incremental area 1705. Another query 1706determines the predominant use in each incremental area based off of thesize of the businesses and/or how many businesses of each land use typeare located within the incremental area. The application 156 then colors1707 each incremental area on the map based on the predominant use 1706in that incremental area. The shade of the color indicates how strongthe use is in the specified area 1602. For example, an incremental areawith 10 office buildings will be shaded dark blue, whereas anincremental area with 1 office building will be shaded light blue. Anincremental area with no businesses will not be shaded any color.

Referring now to FIG. 17B, the method used to produce the Actual LandUse Map 1600 will now be described in more detail.

Referring to 1701 of FIG. 17B, a database query assigns commercial landuse types to each business based upon its phone book category, SIC codesand business name keywords. The user can define a relationship between aphone book category, for example, Attorneys, and a land use type, whichin the case of Attorney would be an Office land use type. In anexemplary embodiment used in a commercial real estate context, land usetypes can be classified as industrial, retail, office and distressed.Distressed represents areas where certain business types indicate lowercommercial quality. The business data may be derived from any sourcethat can be geocoded. Current phone book listings and current businessdirectories provide the means to create an Actual Land Use map thatprovides current utilization of the land.

Referring to 1704 of FIG. 17B, a user or automatic database functionselects the incremental area size to calculate the predominant land usetype. An example could be the size of a city block, another examplecould be a 1/10^(th) of a mile square increment. A spatial query thanreturns all of the businesses that are located in each incremental area.

Referring to 1706 of FIG. 17B, a query determines the predominant landuse in each incremental area. The predominant land use may be calculatedby the following methods: by determining the land use type which has thegreatest number of businesses, or the predominant land use may becalculated using a system of weights. The user may assign custom weightsbased on phone book categories, SIC codes and business name keywords.Normally each business is assigned a weight of 1, but a large businesssuch as a Wal-Mart SuperCenter may receive a weight greater than 1. Theweights from businesses in each land use type are totaled and the landuse type with the greatest total weight is assigned to the incrementalarea.

Referring to 1707 of FIG. 17B, once the predominant land use type isdetermined for the incremental area the incremental area 1601 can becolored on a map 1600. A user may assign a primary color to each landuse type. The user may also assign intensities to the land use types1602. The intensity of each area is determined by the total weight ornumber of businesses used to determine the predominant land use. Theuser assigns a shade of primary color to each land use intensity. Forexample: the user assigns the color orange to the industrial land usetype, and if designating intensities, assigns a light shade of orangefor a lightly used industrial area, and a dark shade of orange for adensely used industrial area. If no businesses exist in the area of thesquare or shape, then no color is assigned to that area. An alternativeaspect of this exemplary embodiment is to shade the no business regionwith a specified color.

A benefit of the Actual Land Use Map is that once each incremental areahas been colored according to the predominant land use, a user such as acommercial site selector can identify areas that are commercialcorridors versus areas which have no commercial development. Forexample, when looking at a plain road map, it is often difficult todiscern which roads in an unfamiliar area have the most commercialactivity. On the Actual Land Use Map, a road with many commercialretailers will have many consecutive incremental areas that are shadedthe assigned retail color, and areas with no commercial development willhave no color shading. Typical downtown areas are often filled with theincremental areas shaded the assigned color for office, because largeskyscrapers or office buildings house many office type tenants.

Another exemplary embodiments of the Actual Land Use Map includes amethod which draws a shape around several incremental areas that have asingle predominant use. For example, a downtown area that has apredominant use as office space, will have many squares or shapes thatare shaded dark blue. The system will draw a shape around the area wherea specified number and percentage of incremental areas are blue. Thiswill help the user determine, amongst other conclusions, where theoffice district is located.

Another exemplary embodiment of the Actual Land Use Map will color thesquares or shapes of each incremental area for other purposes other thana commercial real estate context. For example, the map can be gearedtowards visiting tourists in a city. The map can color shapes or squaresdepending on whether the shape or square has a predominant use oftourist activities. For example, if a shape or square has an art museumor a tourist attraction, it may be shaded red. Areas where there aremany red shapes could have a box or a shape draw around the area, andthis could be called a Tourist District. Hotel locations could besuperimposed over the map, and the user could therefore select a hotelin a tourist district as opposed to selecting a hotel in an officedistrict because the tourist wants to be located near touristactivities. The selection of the hotel on the map could point thetourist to the hotel's website or a reservation booking system, or tellthe user more information about the hotel including price and amenities.

Additional Methods of Viewing Results

Referring now to FIG. 18, in addition to viewing the present inventionon a display, a user can create a printout of the selected analysis. Theuser has several methods to print. A user can select a site on a map606, and choose options to print 1801. A user can use the search featurethat returns sites 1803, and can print the results 1802. A user canselect an individual site to print.

Once the user has selected what sites will be printed, the user ispresented with an option to choose what variables and theircorresponding maps to print 1801. The printing is automated, so that theuser can select the sites to run analysis on, and print, and performother tasks while the system creates maps and analysis and prints theresults for each site.

Possible Sources of Operating Revenue

There are numerous possible sources of operating revenue for the systemoperator using the system in the present invention. The system operatorcan obtain revenue through a subscription service by charging access tothe system. In addition, the system operator can obtain revenue throughinternet based sales; through a subscription service for installationand support of the system on a user's computer; licensing the differenttechnologies to various vendors; click throughs to homes for sale andreal estate commissions; and click throughs to hotel bookings and event,movie, and tourist attraction booking.

An exemplary embodiment of the present invention 100 enables buyers andsellers identify and analyze commercial real estate in an efficient andorganized method. The system and method combines data from a number ofsources and creates an analysis output which allows the user to make arapid, informed commercial real estate decision. The system and methodprovides an efficient and detailed analysis by presenting the user witha method to collect and store data in the field, produce analysis todetermine the viability of a commercial real estate site or project, andsave the results for future display, distribution, and review.

In describing representative exemplary embodiments of the presentinvention, the specification may have presented a method and/or processof the present invention as a particular sequence of steps. However, tothe extent that the method or process does not rely on a particularorder of steps set forth herein, the method or process should not belimited to the particular sequence of steps described. As one ofordinary skill in the art would appreciate, other sequences of steps maybe possible. Therefore, the particular order of the steps set forth inthe specification should not be construed as limitations on the claims.In addition, the claims directed to the method and/or process of thepresent invention should not be limited to the performance of theirsteps in the order written, unless that order is explicitly described asrequired by the description of the process in the specification.Otherwise, one skilled in the art can readily appreciate that thesequences may be varied and still remain within the spirit and scope ofthe present invention.

The foregoing disclosure of exemplary embodiments of the presentinvention has been presented for purposes of illustration anddescription. It is not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Many variations andmodifications of the exemplary embodiments described herein will beobvious to one of ordinary skill in the art in light of the abovedisclosure. The scope of the invention is to be defined only by theclaims, and by their equivalents.

We claim:
 1. A method of displaying a concentration of businesses in asubset of a geographic area by coloring a map based upon a business typeof the businesses, comprising the steps of: defining, in at least onecomputer system, a plurality of commercial land use types according tocompeting business purpose; assigning, in the at least one computersystem, at least one of a plurality of commercial land use types to eachof a plurality of businesses in the subset of the geographic area;associating, in the at least one computer system, a color to each of theplurality of commercial land use types; calculating, in the at least onecomputer system, a predominant commercial land use for a plurality ofareas within the subset of the geographic area from among the pluralityof commercial land use types assigned in the geographic area;generating, in the at least one computer system, the map displaying thegeographical area; and coloring, in the at least one computer system, ina map user interface each of the plurality of areas in the subset of thegeographic area on the map the color assigned to the predominantcommercial land use.
 2. The method of claim 1, wherein the step ofcalculating the predominant commercial land use comprises the step of:totaling, in the at least one computer system, a number of businessesassociated with each of the plurality of commercial land use types ineach of the plurality of areas; and selecting, in the at least onecomputer system, one commercial land use type from among the pluralityof commercial land use types with a greatest number of businesses ineach of the plurality of areas.
 3. The method of claim 1, wherein thestep of calculating the predominant commercial land use furthercomprises the steps of: assigning, in the at least one computer system,a weight to each of the plurality of businesses; and calculating, in theat least one computer system, a total associated with a plurality ofweights assigned to each of the plurality of businesses in eachcommercial land use type; and designating the predominant commercialland use as a one of the commercial land use types having a highesttotal weight.
 4. The method of claim 3, wherein the weight assigned toeach of the plurality of businesses is associated with a size of abusiness.
 5. The method of claim 3, wherein the weight assigned to eachof the plurality of businesses is associated with a character of abusiness.
 6. The method of claim 3, wherein a distressed commercial landuse type is associated with a higher weight relative other commercialland use types.
 7. The method of claim 1, further comprising the stepsof: performing a first plurality of spatial queries against at least onedata source within each of the plurality of areas, each of the firstplurality of spatial queries returning a listing of the businessesassociated with each of the plurality of areas; performing a secondplurality of spatial queries against at least one within each of theplurality of areas, each of the second plurality of spatial queriesreturning a predominant commercial land use associated with each of theplurality of areas.
 8. The method of claim 1, wherein the plurality ofcommercial land use types are one of: industrial, office, retail anddistressed.
 9. The method of claim 8, wherein the plurality ofcommercial land use types further comprise a tourist district.
 10. Themethod of claim 1, further comprising the step of identifying aplurality of incremental areas having a single predominant commercialland use and wherein the step of coloring each of the plurality of areaswithin the subset of the geographic area further comprises drawing ashape around each of the incremental areas.
 11. The method of claim 1,wherein the step of coloring each of the plurality of areas in thesubset of the geographic area on the map the color assigned to thepredominant commercial land use further comprises the steps of:determining a density associated with the predominant commercial landuse; and determining an intensity of the color based at least upon thedensity.
 12. The method of claim 11, wherein the density is based upon anumber of businesses within a respective area that are associated withthe predominant commercial land use.
 13. A system configured to displaya concentration of businesses in a geographic area by coloring a mapbased upon a commercial land use type of the businesses, comprising:means for defining a plurality of land use types according to competingbusiness purpose; means for assigning at least one of a plurality ofcommercial land use types to each of a plurality of businesses in thegeographic area; means for associating a color to each of the pluralityof commercial land use types; means for defining a plurality of areaswithin the geographic area; means for calculating a predominantcommercial land use for each of the plurality of areas from among theplurality of commercial land use types assigned in the geographic area,wherein the calculating means comprises means for assigning a weight toeach of the plurality of businesses, the weight being associated with asize of a business, means for calculating a total associated with aplurality of weights assigned to each of the plurality of businesses ineach commercial land use type, and means for designating the predominantcommercial land use as a one of the commercial land use types having ahighest total weight; means for generating a map user interfaceassociated with the geographical area; and means for coloring each ofthe plurality of areas within the geographic area on the map userinterface the color assigned to the predominant commercial land use. 14.A non-transitory computer-readable medium embodying a program, theprogram configured to display a concentration of businesses in ageographic area by coloring a map based upon a commercial land use typeof the businesses, the program comprising executable code that isexecuted by a computer system and further comprising: code that definesa plurality of land use types according to competing business purpose;code that assigns at least one of a plurality of commercial land usetypes to each of a plurality of businesses in the geographic area; codethat associates a color to each of the plurality of commercial land usetypes; code that defines a plurality of areas within the geographicarea; code that calculates a predominant commercial land use for each ofthe plurality of areas from among the plurality of commercial land usetypes assigned in the geographic area by assigning a weight to each ofthe plurality of businesses, the weight being associated with at leastone of a size of a business, calculating a total associated with aplurality of weights assigned to each of the plurality of businesses ineach commercial land use type, and designating the predominantcommercial land use as a one of the commercial land use types having ahighest total weight; code that generates a map user interfaceassociated with the geographical area; and code that colors each of theplurality of areas within the geographic area on the map user interfacethe color assigned to the predominant commercial land use.
 15. Thenon-transitory computer-readable medium of claim 14, wherein the programfurther comprises: code that performs a first plurality of spatialqueries within each of the plurality of areas, each of the firstplurality of spatial queries returning a listing of the businesses witheach of the plurality of areas; and code that performs a secondplurality of spatial queries within each of the plurality of areas, eachof second plurality of spatial queries returning a predominantcommercial land use associated with each of the plurality of areas. 16.The non-transitory computer-readable medium of claim 14, wherein theweight is further based at least upon whether a business is distressed.17. The non-transitory computer-readable medium of claim 14, wherein adistressed commercial land use type is associated with a higher weightrelative other commercial land use types.
 18. The non-transitorycomputer-readable medium of claim 14, wherein the program furthercomprises code that identifies a plurality of adjacent areas having asingle predominant commercial land use and the code that colors each ofthe plurality of areas within the geographic area further comprises codethat draws a shape around each of the adjacent areas.
 19. Thenon-transitory computer-readable medium of claim 14, wherein theplurality of commercial land use types are one of: industrial, office,retail and distressed.
 20. The non-transitory computer-readable mediumof claim 14, wherein the plurality of commercial land use types furthercomprise a tourist district.